def test_scenario2(self):
"""
Scenario: Successfully creating a model and exporting it:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I export the <"pmml"> model to file "<expected_file>"
Then I check the model is stored in "<expected_file>" file in <"pmml">
Examples:
| data | time_1 | time_2 | time_3 | expected_file | pmml
| data/iris.csv | 10 | 10 | 10 | tmp/model/iris.json | false
| data/iris_sp_chars.csv | 10 | 10 | 10 | tmp/model/iris_sp_chars.pmml | true
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '30', '30', '30', 'tmp/model/iris.json', False],
['data/iris_sp_chars.csv', '30', '30', '30', 'tmp/model/iris_sp_chars.pmml', True]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, example[5], example[4])
model_create.i_check_model_stored(self, example[4], example[5])
def test_scenario2(self):
"""
Scenario: Successfully creating a model and exporting it:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I export the <"pmml"> model to file "<expected_file>"
Then I check the model is stored in "<expected_file>" file in <"pmml">
Examples:
| data | time_1 | time_2 | time_3 | expected_file | pmml
| data/iris.csv | 10 | 10 | 10 | tmp/model/iris.json | false
| data/iris_sp_chars.csv | 10 | 10 | 10 | tmp/model/iris_sp_chars.pmml | true
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '30', '30', '30', 'tmp/model/iris.json', False],
['data/iris_sp_chars.csv', '30', '30', '30', 'tmp/model/iris_sp_chars.pmml', True]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, example[5], example[4])
model_create.i_check_model_stored(self, example[4], example[5])
def test_scenario1(self):
"""
Scenario 1: Successfully creating a local model from an exported file:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I export the "<pmml>" model to "<exported_file>"
When I create a local model from the file "<exported_file>"
Then the model ID and the local model ID match
Examples:
| data | time_1 | time_2 | time_3 | pmml | exported_file
| ../data/iris.csv | 10 | 10 | 10 | False | ./tmp/model.json
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '10', False, './tmp/model.json']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, example[4], example[5])
model_create.i_create_local_model_from_file(self, example[5])
model_create.check_model_id_local_id(self)
def test_scenario1(self):
"""
Scenario 1: Successfully creating a local model from an exported file:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I export the "<pmml>" model to "<exported_file>"
When I create a local model from the file "<exported_file>"
Then the model ID and the local model ID match
Examples:
| data | time_1 | time_2 | time_3 | pmml | exported_file
| ../data/iris.csv | 10 | 10 | 10 | False | ./tmp/model.json
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '10', False, './tmp/model.json']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, example[4], example[5])
model_create.i_create_local_model_from_file(self, example[5])
model_create.check_model_id_local_id(self)
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
